mjagadeeshsai/drug-repurposing-knowledge-graph

This project is a very early-stage (11 days old) prototype with no community traction (0 stars, 0 forks). The approach of using Knowledge Graphs (KGs) and Graph Neural Networks (GNNs) for drug repurposing is a well-established academic and industrial workflow, popularized by projects like Hetionet and PrimeKG. The project's defensibility is low because it appears to be a standard application of existing libraries (likely PyTorch Geometric or DGL) to public datasets. In the drug discovery space, the 'moat' is almost always the quality and uniqueness of the underlying biological data rather than the graph-learning architecture itself. Without a proprietary dataset or a significant algorithmic breakthrough, this repo functions primarily as a portfolio piece or a reference implementation. It faces competition from heavily funded biotech AI companies like BenevolentAI and Recursion Pharmaceuticals, as well as established open-source benchmarks like the Therapeutics Data Commons (TDC). The displacement horizon is short because any specialized bioinformatics team could replicate this functionality in a matter of weeks using standard tooling.